Dakota A Multilevel Parallel Object-Oriented Framework for Design Optimization Parameter Estimation Uncertainty Quantification and Sensitivity Analysis (V.6.14) (Theory Manual)
Year: 2021
Type: report
Abstract: The Dakota toolkit provides a exible and extensible interface between simulation codes and iterative analysis methods. Dakota contains algorithms for optimization with gradient and nongradient-based methods; uncertainty quantication with sampling, reliability, and stochastic expansion methods; parameter estimation with nonlinear least squares methods; and sensitivity/variance analysis with design of experiments and parameter study methods. These capabilities may be used on their own or as components within advanced strategies such as surrogate-based optimization, mixed integer nonlinear programming, or optimization under uncertainty. By employing object-oriented design to implement abstractions of the key components required for iterative systems analyses, the Dakota toolkit provides a exible and extensible problem-solving environment for design and performance analysis of computational models on high performance computers. This report serves as a theoretical manual for selected algorithms implemented within the Dakota software. It is not intended as a comprehensive theoretical treatment, since a number of existing texts cover general optimization theory, statistical analysis, and other introductory topics. Rather, this manual is intended to summarize a set of Dakota-related research publications in the areas of surrogate-based optimization, uncertainty quantication, and optimization under uncertainty that provide the foundation for many of Dakotas iterative analysis capabilities.
DOI: 10.2172/1784843
Authors: Sandia National Laboratories (SNL-NM), Albuquerque, NM (United States), Keith Dalbey, USDOE National Nuclear Security Administration (NNSA), Technical Univ. of Munich (Germany), Univ. of Southern California, Los Angeles, CA (United States), Michael Eldred, Gianluca Geraci, John Jakeman, Kathryn Maupin, Jason Monschke, Daniel Seidl, Laura Swiler, Anh Tran, Friedrich Menhorn, Xiaoshu Zeng
Language: English
Subfield: Statistics, Probability and Uncertainty
Field: Decision Sciences
Domain: Social Sciences
Open Access status: green
OpenAlex ID: W4211211996